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Related Experiment Video

Updated: May 13, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Published on: August 7, 2017

Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification.

Chong-Yaw Wee1, Pew-Thian Yap, Daoqiang Zhang

  • 1Image Display, Enhancement, and Analysis (IDEA) Laboratory, Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA, cywee2000@gmail.com.

Brain Structure & Function
|March 8, 2013
PubMed
Summary

This study introduces a new method for analyzing brain connectivity using resting-state functional magnetic resonance imaging (R-fMRI) to improve disease classification. The approach ensures consistent brain network structures across individuals, enhancing early detection of neurological conditions like mild cognitive impairment.

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Last Updated: May 13, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Neuroscience
  • Network Science
  • Medical Imaging

Background:

  • Resting-state functional magnetic resonance imaging (R-fMRI) offers insights into neurological disorders by analyzing whole-brain connectivity.
  • Inferring accurate brain connectivity from R-fMRI is challenging due to noise, high dimensionality, and inter-subject variability.
  • Individual sparsity constraints in connectivity modeling can increase variability and reduce classification performance.

Purpose of the Study:

  • To develop a novel group-constrained sparse modeling approach for inferring topologically identical brain connectivity networks across individuals from R-fMRI data.
  • To enhance the classification performance in identifying neurological disorders, particularly in early stages.

Main Methods:

  • Formulated R-fMRI time series of each region of interest (ROI) as a linear representation of other ROIs.
  • Applied L1-norm for individual sparsity to filter connections and L2-norm group-constraint via multi-task learning for consistent network topology across subjects.
  • Validated the model using mild cognitive impairment identification.

Main Results:

  • The proposed method achieved promising results in mild cognitive impairment identification, demonstrating superior disease characterization and sensitivity to early-stage pathologies.
  • Inferred group-constrained sparse networks were biologically plausible and associated with disease-related anatomical anomalies.
  • Similar classification performance was maintained even with finer brain parcellation (atlas).

Conclusions:

  • The developed group-constrained sparse modeling approach effectively infers consistent brain network topologies from R-fMRI data.
  • This method improves the characterization and early detection of neurological disorders by addressing inter-subject variability and noise.
  • The findings highlight the potential of this technique for advancing neurological disorder research and clinical applications.